Challenges and Innovative Solutions for Bridge Foundation Repairs
Bibliographic record
Abstract
The Province of Manitoba experienced a record flood in the spring of 2011 and a record heavy rainfall event in the summer of 2014. The flooding occurred on major and minor rivers, including the Assiniboine River, Souris River, and Portage Diversion channel. These unprecedented floods produced higher than normal water levels, flows and velocities, reaching levels of three times design values at several locations. The flood events impacted over 100 bridge structures on the provincial highway network, crippling local access and transportation in the areas. Several bridges required complete replacement, while others experienced significant foundation undermining, instability and overall risk of collapse due to significant scour. Record water levels and turbulent flows caused significant erosion and scour resulting in undermined spread footings and exposed foundation piles. Five bridges contended with high risk of foundation loss beneath river piers, requiring emergent repair solutions to re-establish full original design loading and prevent any further risk during future flooding. The innovative, yet challenging rehabilitation strategy was to provide increased foundation capacities and stability using deep foundation methods. Driven piles were not considered a viable option in most cases, due to existing sub-surface stratigraphy, the potential of shallow depth refusal and potential for achieving insufficient capacities. Various drilled caisson techniques were primarily used. During construction, many challenges were encountered due to site conditions, high water flows and geotechnical restraints. Special consideration was made to avoid disturbance and softening of shale material within and around the drilled holes, working adjacent to the existing structure with traffic still using the bridge, and to maintain temporary access and slope stability of the riverbanks. The most challenging aspect of the assignment was working within tight construction timeframes and adapting foundation design as issues arose, in order to complete all construction and protection measures prior to the potential for next year’s spring flood event. This paper presents how these structures were saved using innovative rehabilitation strategies and unique repair methods to restore and increase foundation capacities, and to provide overall structural stability for five bridge structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".